Capital Market and Business Cycle Volatility · Capital Market and Business Cycle Volatility Piyapas Tharavanij1 ... reducing inequality of access and financial imperfection are necessary

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Capital Market and Business Cycle Volatility

Piyapas Tharavanij1

Department of Economics Faculty of Business and Economics

Monash University

Phone: 61 3 9905 8607 Email: [email protected]

1The author would like to thank Prof.Dietrich Fausten and Assoc. Prof. Mark Harris for invaluable suggestion and encouragement.

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Preface

Title of Thesis: Capital Market and Business Cycles

Supervisor: Prof.Dietrich Fausten and Assoc. Prof. Mark Harris

The thesis is structured as follows:

• Chapter 1: Introduction

• Chapter 2: Literature Reviews

• Chapter 3: Theoretical Foundations of Financial Structure, Financial

Development, and Business Cycles

• Chapter 4: Measurement Issues

• Chapter 5: Capital Market and Business Cycle Volatility

• Chapter 6: Capital Market, Severity of Business Cycle, and Probability of

Economic Downturn

• Chapter 7: Capital Market, Frequency of Recession, and Fraction of Time the

Economy in Recession

• Chapter 8: Conclusion

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Abstract This paper investigates cross-country evidence on how capital market affects

business cycle volatility. In contrast to the large and growing literature on the impact

of finance and growth, empirical work on the relationship between finance and

volatility has been relatively scarce. Theoretically, more developed capital market

should lead to lower macroeconomic volatility. The major finding is that countries

with more developed capital market have smoother economic fluctuations. Results are

generated using panel estimation technique with panel data from 44 countries

covering the years 1975 through 2004.

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Capital Market and Business Cycle Volatility

1. Introduction

The role of financial development in economic growth and stability has, for many

years, been the subject of intense discussion and debate among both academicians and

policy makers. The mainstream view [Demirguc-kunt and Levine (2001)] is that

financial development exerts a large positive impact on economic growth. Moreover,

this link holds even after controlling for other growth determinants and possible

endogeneity. Many researchers also have sought to evaluate the links between capital

market development (usually stock market) and growth, or between a relative measure

of capital market development as captured by a financial structure index which

measures the degree to which a financial system is bank-based or market-based, and

growth. Interestingly, Demirguc-kunt and Levine (2001) found that financial structure

does not have any explanatory power after controlling for the level of overall financial

development. However, Beck and Levine (2002) do find that stock markets and banks

are both individually significant in explaining economic growth. This would suggest

that both markets and banks independently spur growth, and that stock markets

provide different financial services from banks.

Traditional explanations of the connection between financial development and

volatility are based prominently on the phenomena of credit market imperfections and

asymmetric information. The “balance sheet view” [Bernanke and Gertler (1995),

Bernanke et al. (1998)] postulates that nominal and real shocks to the economy are

amplified by a “financial accelerator.” Basically, the fall in a firm’s net worth

resulting from an initial shock (say, from a monetary contraction) increases agency

costs by worsening the potential conflicts of interest between borrowers and lenders.

This leads subsequently to higher external financing premiums, which in turn magnify

the fluctuations in borrowing, spending and investment. Therefore, to the extent that a

more advanced financial system reduces this imperfection, it decreases the volatility

of business cycles. Greenwald and Stiglitz (1993) also argue that efficient financial

markets mitigate information asymmetries and enable economic agents to process

information more effectively, resulting in lower growth volatility.

Unlike traditional theory, recent explanations focus more on specific

mechanisms rather than on asymmetric information. For instance, Aghion et al.

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(1999) show theoretically that combining financial market imperfections with unequal

access to investment opportunities across individuals can generate endogenous and

permanent fluctuations in aggregate GDP, investment, and interest rates. Thus,

reducing inequality of access and financial imperfection are necessary conditions for

macroeconomic stability. In another important contribution Acemoglu and Zilibotti

(1997) argue that the presence of indivisible projects limits the degree of

diversification that an economy can achieve in the early stages of development,. The

inability to diversify idiosyncratic risk, and the desire to avoid high risk investments,

slow down capital accumulation and introduce large uncertainty into the growth

process. By providing a closer match between savers and investors and promoting

diversification, financial deepening reduces risk and dampens cyclical fluctuations.

Larrain (2004) develops a theoretical model which predicts that the effect of

financial development on output volatility is ambiguous. The model shows that the

effect depends on particular circumstances that constrain, firm financing's decisions.

If firms need funds to smooth unfavourable cash-flow shocks, financial development

reduces output volatility. In contrast, if firms need funds to expand production when

confronted with positive investment opportunities, financial development increases

output volatility. Thus, knowing whether the effect of financial development on

volatility is positive or negative permits inferences to be drawn about the type of

shock that firms are facing.

Theoretically, the resilience of an economy is affected not only by the overall

level of financial development but also by its financial structure - whether it is bank-

based or market-based. Rajan and Zingales (2001) observe that “if there is one thing

the arm’s-length system (market-based) can do better than the relationship-based

(bank-based), it is to bear and manage macroeconomic risk.” They argue that due to

low transparency and disclosure, assets in a bank-based system tend to be less liquid.

Intermediaries (mainly banks) finance such assets by low cost demand deposit. This

exposure makes them subject to runs. In other words, financing of illiquid assets in a

bank-based system is likely to create a maturity mismatch in the portfolios of

intermediaries. This financial fragility of intermediaries would then impose risk on the

financial system.

They argue further that should a relationship-based system suffer adverse

shocks that the government is not able to counter, then the flow of credit can quickly

collapse. They give the following reasons. First, there is a lot of specific knowledge

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embedded in relationships between failing intermediaries and their clients. Therefore,

other healthy intermediaries cannot easily replace them in providing any further credit

to debtors of the failing ones. Secondly, since property rights are not well established

in non-transparent relationships, it becomes hard for depositors and investors to

distinguish between healthy and failing parties. This could lead to financial contagion

among intermediaries and also give rise to bank runs.

In contrast, in market-based systems, transparency and disclosure are required

to give investors the confidence to invest directly in particular firms. This greater

transparency improves the ability of a system to withstand shocks. Healthy firms can

be distinguished from the terminally ill after a shock and can be dealt with differently.

As a result, outside investors or intermediaries have the ability to invest and rescue

the system from the consequences of failing financial intermediaries.

Haan et al. (1999) extend these ideas by developing a formal model of the

propagation of business cycle shocks, given the existence of long-term relationships

between entrepreneurs and lenders (which are more prevalent in bank-based system).

Lenders may be constrained in their short-run access to liquidity, and when liquidity

is low, relationships are subject to break-ups that lead to loss of joint surplus. In this

way, feedbacks between aggregate investment and the structure of intermediation

greatly magnify the effects of shocks. The authors show that, for large shocks,

financial collapse is unavoidable unless external interventions occur.

Fecht (2004) developed a theoretical model which shows that in a market-

based system, banks only provide access to efficient investment to unsophisticated

households, whereas in a bank-based system bank deposit contracts also offer some

degree of liquidity insurance. Consequently, in a bank-based system the household

sector holds a larger portfolio of deposits and a smaller part in corporate investment.

Fecht argues that moderately bank-dominated financial systems are fragile because

fire sales of a single troubled bank can more readily cause asset-price deterioration

that propels other banks into crisis. Conversely, fire sales by distressed banks are

unlikely to cause a sudden drop in asset prices sufficiently large to trigger financial

contagion in either market-oriented or extremely bank-dominated financial systems.

In market-based financial systems, financial markets are deep and able to absorb fire

sales with limited impact on prices. Alternatively, in strongly bank-dominated

financial systems banks’ transactions in secondary financial markets affect only a

rather limited segment of their balance sheets. Therefore, banks’ market exposure is

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comparatively small and they are able to buffer fire sales that have a severe impact on

asset prices when markets are relatively illiquid. In contrast, in moderately bank-

based financial systems banks depend on liquidity inflow from assets sales and are

therefore more vulnerable to adverse price movements. Banks face considerable

difficulty in compensating for the shortfall of liquidity inflows after the fire sales.

Empirical studies on the impact of financial development or capital market on

macroeconomic variability provide only mixed support of the hypothesis that higher

financial or capital market development leads to lower volatility. Silva (2002) applied

generalized method of moments technique on cross-sectional data set and found that

countries with more developed financial systems had smoother business cycle

fluctuations. Interestingly, the inclusion of dummy variables representing bank-based

or market-based financial structure does not affect the result and the coefficient is not

significantly different from zero. Lopez and Spiegel (2002) found a significant

negative relationship between financial development and income volatility from a

cross-country panel, suggesting that financial development does mitigate economic

fluctuations in the long run.

Denizer et al. (2000) estimated fixed effects regressions with panel data and

found that countries with more developed financial sectors experience smaller

fluctuations in real per capita output, consumption, and investment growth.

Phumiwasana (2003) empirically investigated relationships between financial

structure, volatility, and economic growth. Using panel regressions, he found

evidences that bank-based financial system increases the growth volatility among

developed countries, while decreases growth volatility among developing countries.

Using cross-industry and cross-country data, Raddatz (2003) estimated the

effect of financial development on volatility based on differences in sensitivity to

financial conditions across industries. The results show that sectors with larger

liquidity needs are more volatile and experience deeper crises in financially

underdeveloped countries. The result suggests that changes in financial development

can generate important differences in aggregate volatility. This finding also provides

indirect support to the theory that development of financial markets reduces

macroeconomic volatility because it increases the ability of intermediaries to provide

liquidity during periods of distress. Moreover, he found that the development of

financial intermediaries is more important than the development of equity market for

the reduction of volatility.

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In contrast to existing literatures, which primarily focus on the relationship

between financial development and growth, this paper examines empirical

relationships between capital market, financial development, and macroeconomic

variability. The paper finds that output and investment volatilities are negatively

related to measures of capital market development after controlling for other relevant

variables. In addition, there are also some evidences that capital market development

also lower consumption volatility. Empirical results support the theoretical prediction

that capital market development would lead to lower volatility.

The organization of this paper is as follows. Section 2 discusses measurement

issues. Section 3 discusses data construction and data description. Section 4 provides

methodology. Section 5 presents estimation results. Section 6 discusses robustness

issues. Lastly, section 7 covers policy implications, and conclusion.

2. Measurement Issues

Financial Development

Ideally, one would like measures of financial development, which indicate the degree

to which the financial system ameliorates information asymmetry and facilitates the

mobilization and efficient allocation of capital. Particularly, one would prefer

indicators that capture the effectiveness with which financial systems research firms

and identify profitable investment, exert corporate control, facilitate risk management,

mobilize saving, and ease transaction [Merton and Bodie (2004)]. Unfortunately, no

such measures are available. As a result, one must rely on several proxies of financial

development that existing empirical work shows are robustly related to economic

growth or other components of aggregate output.

The most commonly used measure of financial development [e.g. Levine and

King (1993), Denizer, et al. (2000)] is "Private Credit", defined as the ratio of

domestic credit extended to the private sector by financial intermediaries to GDP.

More specifically, domestic credit to private sector refers to financial resources

provided to the private sector, such as through loans, purchases of nonequity

securities, and trade credits and other accounts receivable, that establish a claim for

repayment. This measure captures the amount of credit channelled through financial

intermediaries to the private sector. Beck et al. (2000) show that Private Credit is a

good predictor of economic growth and the positive correlation between the two is not

due to reverse causality.

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The alternative measure is the "Liquidity Ratio", defined as the ratio of liquid

liabilities (usually M3) to GDP. Levine and King (1993) introduce this variable under

the name "Financial Depth" to proxy for the overall size of the formal financial

intermediary sector relative to economic activity. However, such monetary

aggregates do not differentiate between the liabilities of various financial institutions,

and may not be closely related to financial services such as risk management and

information processing [Levine and King (1993)].

This study uses "Private Credit" as a primary measure of financial

development. However, it also employs the "Liquidity Ratio" as an alternative

measure for robustness check.

Capital Market

Measures of capital market development can be broadly classified into two

categories: absolute and relative measures. An absolute measure identifies the level of

capital market development itself without reference to other developments in the

financial system. Alternatively, a relative measure attempts to measure the importance

of direct financing via capital markets relative to indirect financing via financial

intermediaries, particularly banks. These measures were first developed to classify

financial systems as bank-based or market-based systems [Levine (2002)]. Given that

these relative measures compare different components of the financial system, they

can be used as measures of financial structure.

Absolute measures of capital market development usually involve the size and

liquidity of stock markets and/or bond markets [Beck and Levine (2002)]. Most cross-

country studies use only stock market data because bond market data are usually not

available for emerging economies. The standard measure is the "Turnover Ratio",

defined as the value of shares traded on domestic exchanges divided by the total value

of listed shares. Basically, it indicates the trading volume of the stock market relative

to its size. One advantage of this measure is that it is relatively immune to business

cycle and asset price fluctuation because prices appear both in the numerator and the

denominator. An alternative measure is "Value Traded", defined as the value of the

trades of domestic shares on domestic exchanges divided by GDP. It measures trading

relative to the size of the economy. Since value traded is the product of quantity and

price, this indicator could rise just from favourable expectation of the future without

any increase in transactions activity. Turnover ratio does not suffer from this

shortcoming. The other alternative measure is "Capitalization Ratio", defined as the

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total stock market capitalization over GDP. This measure suffers the same weakness

as "Value Traded". This paper uses "Turnover Ratio" as an absolute measure of

capital market development and uses "Value Traded" and "Capitalization Ratio" as

alternative measures for robustness checks.

Relative measures of capital market development gauge the development of

capital markets relative to that of financial intermediaries, particularly the banking

sector. In the literature they are known as measures of "Financial Structure",

indicating whether the financial system is market-based or bank-based. Since there is

no single accepted definition of financial structure, Beck et al. (2001) construct

several indicators where higher values indicate that a financial system is more market-

based. They aggregate these indicators into a single financial structure index. The first

indicator is Structure-Activity, which measures stock market activity relative to that

of banks. It is defined as the log of the ratio of Value Traded (defined as “value of

total shares traded on the stock market divided by GDP”) over Bank Credit (defined

as “the claims of the banking sector on the private sector as a share of GDP”).The

second indicator is Structure-Size, which compares the sizes of the stock market and

the banking sector. Specifically, it is defined as the log of the ratio of Market

Capitalization and Bank Credit. Market Capitalization is defined as "the value of

listed shares divided by GDP." Bank Credit represents the claims of the banking

sector on the private sector as a share of GDP. Compared to Private Credit, this

measure focuses on the commercial banking sector only, excluding the claims of non-

bank financial intermediaries. Levine (2002) also proposed another indicator,

Structure-Efficiency, defined as the log of the value traded ratio multiplied by

overhead costs. Overhead costs equal the overhead costs of the banking system

relative to banking system assets.

The aggregate measure of financial structure is the Structure-Aggregate index

which combines the three previous measures. Specifically, it is the first principal

component of Structure-Activity, Structure-Size and Structure-Efficiency. In previous

studies [e.g. Levine (2002)], countries with a Structure-Aggregate index higher or

equal to the sample mean are classified as having a market-based financial structure.

Conversely, countries with an index lower than the sample mean are classified as

having a bank-based financial structure.

This study uses the "Structure-Aggregate index" as a relative measure of

capital market development. However, the structure-aggregate index was constructed

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as the first principal component of structure-activity and structure-size indices only.

The reason is that data required to construct the structure-efficiency index are not

available for a number of countries and periods.

The "Financial Structure Aggregate Index" is used mainly for robustness

check, and more importantly for a comparison purpose with an absolute measure of

capital market development, turnover ratio. By using the index as a relative measure

of capital market development, the applied methodology here related financial

structure and growth literature with this study. The interpretation of results in this

study should not be that a country should pursue any particular form of "financial

structure" (bank-based or market-based), but rather whether a country also need well-

developed capital markets, and not only financial intermediaries, to achieve more

stable financial system and lower volatilities.

Business Cycle Volatility

There are two standard measures of business cycle volatility of output, namely

standard deviation of growth rates of real GDP per capita, and standard deviation of

business cycle components (filtered components) of a similar variable. In the first

approach, growth rate is calculated by taking log difference. The second approach

[e.g. Tiryaki (2003)] focuses on the magnitude of business cycle as a measure of

macro-variability. The business cycle components are estimated using filtering

technique [e.g. Hodrik-Prescott filter, Bakter-King filter]. This method is widely used

among macroeconomist to smooth out business cycle.

This paper applied both approaches in measuring business cycle volatility. The

filtering technique applied is Chistiano-Fitzgerald (CF) band-pass filters, which

extract cyclical variations that last 2 to 8 years. Cyclical fluctuations in this frequency

are widely considered to be associated with the business cycle [Haug and Dewald

(2004)]. The applied filter was suggested by Christiano and Fitzgerald (2003). This

filter uses a non-symmetric moving average with changing weights. Every

observation of a time series is filtered using the full sample. Another popular filter is

the Hodrick and Prescott (1997) filter. This filter amplifies the cyclical component

and downplays the high frequency noise, but it still passes much of the high-

frequency noise outside the business cycle frequency [Stock and Watson (1998)]. The

alternative band-pass filter that could also extract fluctuation from the 2 to 8 years

frequency is Baxter and King (1995) filter. This filter is a symmetric centered moving

average, where the weights are chosen to minimize the squared difference between

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the optimal and approximately optimal filters. The drawback of this filter, however, is

that there would be loss of data at the beginning and ending of the series.

For components of aggregate output, this paper use standard deviation of gross

capital formation growth rate and standard deviation of household consumption

growth rate as measures of investment and consumption volatility respectively.

3. Data

The panel covers annual data of 44 countries from 1975 to 2004. Data sources are

International Financial Statistics (IFS), World Development Indicators (WDI), Barro-

Lee data set [Barro and Lee (2000)], Legal Origin and Creditor's Protection data set

[La-Porta et al. (1998)], and Financial Structure data set [Levine (2002)]. Variable

description and name list of countries in the sample classified by income level are in

Appendix A and in Appendix B respectively. The annual data are transformed into six

five-year-span panel data. Therefore, period 1 covers the years 1975-1979, period 2

covers 1980-1984, period 3 covers 1985-1989, period 4 covers 1990-1994, period 5

covers 1995-1999, and finally period 6 covers 2000-2004.

The transformation method is usually just the average, but for variables that

measure volatilities (such as growth, or changes in terms of trade); the transformation

involves the calculation of standard deviation of that variable within that particular

observation period. Moreover, for robustness check, measures of financial

development, capital market development, and income level are also transformed by

using the initial values within the period.

The transformed variables are based on available annual data. Where the

original annual data set shows missing data in certain years the transformations have

been calculated if there are at least three valid data points for a given five-year time

span. That criterion implies that more than 50% of observations for a given time-span

are valid. Otherwise, the data are considered missing for that particular observation in

the panel.2

Table 1 shows business cycle volatilities across countries. Economic

performance differs widely. This is true not only with growth rate but also with

2 For example, the first five-year period runs from 1975-1979. If there are, say, four annual observations for variable X1 covering the years 1976-1979, then the transformation of those data into the panel is performed by averagingtheir values. However, if the observations onX1 cover only less than three years in any relevant five-year interval, say 1978-1979, then the relevant data point in the panel is listed as “n.a.:” (not available). This practice avoids loosing too many data points in the panel construction while the transformed data are still representative of the corresponding years.

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growth rate volatility and business cycle component volatility. Growth volatilities

vary widely from very volatile of 10.2% to almost steady of 0.3%. High income

countries tend to have lower both growth volatility and business cycle volatility.

Table 2 shows capital market development among countries from last period

in the panel (year 2000-2004). Higher income countries tend to have more financial

development, measured by private credit ratio. In addition, higher income countries

also tend to have more market-based financial structure, measured by Financial

Structure index. Interestingly, turnover ratio, as a measure of capital market

development, does not have a stable relationship with income.

Table 3 and 4 provide descriptive statistics and correlations, respectively.

Please note that many variables are already in log form (see Appendix A for variable

description). Both measures of business cycle volatility, namely growth volatility (g-

vol) and business cycle component volatility (b-vol) are highly positively correlated (r

= 0.91). Both, investment volatility (i-vol) and consumption volatility (c-vol) are

positively correlated with both measure of output volatility (g-vol, b-vol) with

correlations of 0.45 and 0.40 respectively. Interestingly, both investment and

consumption volatility are relatively highly correlated with correlation of 0.68.

All volatilities (g-vol, b-vol, i-vol, c-vol) are negatively correlated with

financial development (credit), income (gdp), and capital market development

(turnover), and market-based financial structure (struc). This implies that countries

with higher financial development, more advanced capital market, and higher income

tend to have lower growth volatility and lower business cycle component variations.

Capital market development (turnover) is positively correlated with financial

development (credit), and income (gdp). This means that countries with higher

financial development and higher income tend to have more advanced capital market.

Income (gdp) and financial development (credit) is positively correlated. The

correlation is 0.58. This means that countries with high income tend to have more

developed financial system.

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4. Methodology

The estimated model is a reduced-form equation relating volatility, financial

development, and capital market development.

σit = β0 + β1.FDit + β2.FSit + β3.Xit + εit

σit is a measure of volatility. Depending on the specification, it could be log of

standard deviation (sd.) of growth rate of output (g-vol), investment (i-vol), or

consumption (c-vol), or sd. of CF-filtered log of output (b-vol). FD is a measure of

financial development, namely log of private credit ratio (credit). FS is a measure of

capital market development. An absolute and a relative measure would be log of

turnover ratio (turnover) and financial structure-aggregate index (struc), respectively.

X is a vector of standard controlled variables [see e.g. Lopez and Spiegel (2002),

Beck et al. (2003)]

The above reduced-form equation would be estimated by panel estimation;

including pooled, random effects and fixed effects with robust variance [see e.g.

Greene (2003) pp.314-318]. Furthermore, to take into account possible reverse

causalities and endogeneity problems of financial development or capital market

development, initial value of suspected variables instead of the average values of each

sub-period will also be used in the estimation. In addition, instrumental variable

estimation would be performed for robustness checks. Instruments for financial

development are time trend, legal origin and creditor's protection index [La-Porta, et

al. (1998)]. In case of panel instrumental variable estimation, instruments are time

trend, creditor's protection index, and human capital index. Controlled variables (X)

include the following.

Income Level [log of real gdp per capita (gdp)]

The level of income is included to control for the fact that developing

countries tend to experience much more volatility than developed countries [Easterly

et al. (2000)]

Openness [log of openness ratio (openness)]

The effect of trade openness on volatility is ambiguous. On one hand,

reductions of barriers to trade may increase countries’ susceptibility to external

shocks. On the other hand, trade with other countries can reduce the impact of

domestic shocks. This volume variable is measured by the share of trade (export +

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import) in GDP. Our analysis does not include any measure of financial openness

since the empirical literature [e.g. Buch et al. (2005)] has not been able to establish a

statistically significant link between financial openness and business cycle volatility.

Government Consumption Spending over GDP (gcon)

There is general consensus at least among Keynesian macroeconomists that

government has a role in promoting economic stabilization. Fiscal policy is an

effective tool to counter business cycles. The mean of government consumption

spending over GDP is included to take this fact into account.

Standard Deviation of Changes in Real Effective Exchange Rate (sd-dreer)

One intensely debated topics of international macroeconomics is which

exchange rate regime (fixed or floating) promotes greater stability of output. The

answer depends on the type of shock that hits the economy. A fixed exchange rate is

better if monetary shocks dominate, whereas floating is better if real shocks

dominates [Karras and Song (1996)]. The standard measure of exchange-rate

flexibility is the standard deviation of the real effective exchange rate. The data can be

obtained from International Financial Statistics (IFS).

Standard Deviation of Changes in Terms of Trade (sd-dtot)

The standard deviation of changes in the terms of trade is a proxy for the

extent to which an economy is exposed to real shocks. Raddatz (2005) finds that

among low-income countries, changes in commodity price are the most important

external shocks. However, since changes in the terms of trade affect the economy

through relative price movements of imported input and exported output, they only

affect the tradable sector of an economy directly, whereas the non-tradable sector

might be affected only indirectly. Therefore, countries with large non-tradable sectors

will be relatively less affected by fluctuations in the terms of trade. This fact is

controlled for by including an openness ratio (ratio of trade over GDP) in the analysis.

Legal Origin

Legal systems with European origin can be classified into four major families:

the English Common Law and the French, German, and Scandinavian Civil Law

countries. Civil Law has its root in Roman law, and uses primarily legal codes to

resolve particular cases. Unlike Civil Law, the English legal system is based on the

Common Law where judges primarily formed the law in the course of trying to

resolve particular cases. La-Porta, et al. (1998) show that common law countries

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generally have the best, and French civil law countries the worst, legal protection of

investors, with German and Scandinavian civil law countries located in the middle.

Since most countries have acquired their legal system through occupation and

colonization, legal origin can be regarded as relatively exogenous. In addition, La-

Porta, et al. (1998) have shown that the legal origin of a country materially influences

the rights of its creditors and shareholders, its accounting standards, and the efficiency

of contract enforcement. Furthermore, Levine et al. (1999) have shown that legal

origin explains cross-country variations in the level of financial development.

Creditor’s Protection

The creditor protection index shows how well a country protects the claims of

secured creditors in the case of company restructuring or liquidation. It ranges from 0

to 4 and is composed of four dummy variables that indicate whether (1) the

restructuring procedure imposes an automatic stay on assets that prevents secured

creditors from taking possession of loan collateral; (2) secured creditors are ranked

first in the case of liquidation; (3) management does stay in charge of the firm during

restructuring, thereby enhancing creditors’ power; and (4) management needs

creditors’ consent when filing for restructuring. Basically, higher values of Creditor

Protection mean that outside investors have more rights relative to the management

and other stakeholders. This implies that outside investors should be more willing to

provide external finance.

5. Estimation Results

Table 5 and 6 show estimation results of growth volatility (g-vol) using turnover ratio

(turnover) and financial structure aggregate index (struc), respectively. In pooled

estimation, both measures are negatively significant. This suggests that higher capital

market development is associated with lower growth volatility. Interestingly, private

credit (credit), a measure of financial development, and income level (gdp) are not

significant, though still have negative signs as expected. These results still hold after

controlling for random individual effects (in RE estimation). However, using

Hausman test, we reject the null hypothesis of zero correlation of individual effects

and other predictors (random effects assumption) in favour of fixed effects estimation,

which does not rely on this assumption. In fixed effects estimation, we rejected the

hypothesis of no individual effects, using F-statistic. Furthermore, previous results

still hold, and coefficients of turnover ratio (turnover) and structure index (struc) are

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nearly double. To avoid reverse causality, initial values of turnover ratio, financial

structure index, private credit, and income level were used in fixed effects estimation

(FEI). The results are still consistent with previous findings.

To take into account possible endogeneity of financial and capital market

development, instrumental variable estimation (IV) were conducted. Turnover ratio

(turnover) and structure index (struc) are still negatively significant and the

coefficients are even more negative than those in pooled estimation. The last columns

report results from fixed effects IV estimation (IVFE). Unlike in normal fixed effects,

we cannot reject null hypothesis of no individual effects, using F-statistic. This

validates previous results from IV estimation. Interestingly, both turnover and struc

are not significant, though still have negative signs.

Among other explanatory variables, only trade openness ratio (openness) is

consistently significant across various estimation methods. After controlling for

country fixed effects, higher trade openness is associated with lower growth volatility.

Table 7 and 8 show estimation results of business cycle component volatility

(b-vol) using turnover ratio (turnover) and financial structure aggregate index (struc),

respectively. With turnover ratio (turnover) as an absolute measure of capital market

development, results are broadly similar to previous cases of growth volatility.

Turnover ratio is consistently significant with negative signs across different

estimation methods except in fixed effects instrumental variables estimation (IVFE).

Both Hausman and F statistics justify the use of fixed effects. Using Hausman

statistic, we reject null hypothesis of zero correlation between individual effects and

other predictors, and using F statistic, we reject null hypothesis of no individual

effects.

In sharp contrast, structure index (struc) as a relative measure of capital

market development, is not significant under most estimation methods except in fixed

effects using initial value data (FEI) and instrumental variable estimation (IV).

However, the signs are consistently negative.

Surprisingly, private credit (credit), a measure of financial development, is

almost always not significant. Income level (gdp) is negatively significant in pooled,

random effects, and IV estimation, but becomes insignificant with positive signs after

we controlled for fixed effects.

Among other explanatory variables, trade openness ratio (openness) and real

effective exchange rate volatility (sd-dreer) are consistently significant. Similar to the

Page 18 of 36

case of growth volatility, higher trade openness is associated with lower business

cycle component volatility after controlling for country fixed effects. On the other

hand, higher real exchange rate volatility is consistently associated with higher

volatility of business cycle.

Table 9 and 10 show estimation results of investment volatility (i-growth)

using turnover ratio (turnover) and financial structure aggregate index (struc),

respectively. Both measures are negatively significant across all estimation methods.

Using Chi2 statistics in random effects estimation, and F statistics in fixed effects

estimation, we rejected null hypothesis of no individual effect. From Hausman

statistics, we cannot reject hypothesis of zero correlation between individual effects

and other predictors. In this case, both random and fixed effects estimators are

consistent, but random effects estimator is also efficient. Interestingly, turnover ratio

(turnover) and financial structure index (struc) are negatively significant even in fixed

effects instrumental variable estimation (IVFE).

Private credit (credit), a measure of financial development, is negatively

significant in both pooled and random effects estimation. However, though still has

negative signs, it became insignificant once we controlled for possible endogeniety in

IV estimation. Income level (gdp) is not significant in any estimation.

Among other explanatory variables, only real effective exchange rate volatility

(sd-dreer) is consistently positively significant across various estimation methods. The

results suggest that higher real exchange rate volatility is associated with higher

investment volatility.

Table 11 and 12 show estimation results of consumption volatility (c-vol)

using turnover ratio (turnover) and financial structure aggregate index (struc),

respectively. Though, both measures are significant under certain estimation methods,

there is no evidence of robust relationship. Income level (gdp) is negatively

significant under most estimation methods. This result seems to suggest that rich

countries have better ways to smooth out consumption variability. Private credit, a

measure of financial development, is not significant under any estimation method.

Other explanatory variables are also not consistently significant, except real exchange

rate volatility (sd-dreer), which is positively significant when financial structure index

(struc) is used as a measure of capital market development.

Page 19 of 36

6. Robustness Check

For robustness check, estimations are also performed using alternative measures of

financial and capital market development. More specifically, liquidity ratio

(M3/GDP) is used instead of private credit ratio (private credit/GDP) to measure a

degree of financial development. Value traded ratio (stock value traded/GDP) and

market capitalization ratio (stock market capitalization/GDP) are used instead of

turnover ratio (stock value traded/stock market capitalization) as a measure of capital

market development. The results, not reported here, are that major findings from

previous sections do not materially change with alternative measures. In both growth

volatility and investment volatility regressions, coefficients of value traded ratio and

market capitalization ratio are consistently significant with negative sign. However,

they are not significant in explaining consumption growth volatility, but this is the

same result we found with turnover ratio and private credit ratio.

Other plausible relevant variables (e.g. standard deviation of inflation, average

inflation rate, and investment ratio) are also included in the estimation, but have never

been significant. Therefore, they are dropped from the reported tables.

7. Policy Implications and Conclusion

The above econometric analysis supports theoretical prediction that the development

of capital markets reduces output, investment, and consumption volatilities. The

coefficients of alternative measures of capital market development are significant in

most specifications with negative signs. Nevertheless, the values of the coefficients

are rather small, always less than unity. This raises the question whether the effect of

capital market development on aggregate volatility is economically meaningful, even

if it is statistically significant.

To investigate the above question, the simple calculation below use a

coefficient of log of turnover ratio (turnover) from fixed effects estimation (FEI) of

growth volatility in Table 5 as a benchmark. The coefficient is -0.16. The inter-

quartile range of turnover ratio in the sample is 49.36. In terms of log difference, it is

1.67. Therefore, the effect of an inter-quartile improvement in turnover ratio is -0.27

(-0.16 * 1.67) or a reduction of 27% of volatility (note that: the left-hand side variable

is log of volatility). The average growth volatility is 2.1%. Therefore, a decrease of

27% would mean a decrease of 0.50 percentage point (2.1-2.1*exp(-0.27)) in standard

deviation of growth rate.

Page 20 of 36

In summary, capital market does exert a statistically significant influence on

volatility, and the magnitude of the decrease in volatility is quite large. However,

when we measure the change in absolute terms as the proportionate change of the

standard deviation of growth, then the size of the effect seems to be quite small,

approximately half a percentage point.

To conclude, this chapter investigates the effect of capital market development

on output, investment and consumption volatilities in forty-four countries using data

from 1975 to 2004 period. The main result is that output, investment and consumption

volatilities are negatively related to measures of capital market development after

controlling for other relevant variables. Hence, the empirical findings corroborate the

theoretical prediction that more advaced capital market is associated with lower

volatilities.

Interestingly, econometric analysis here could not find robust negative

relationship between financial development and output volatility as in previous

studies [e.g. Silva (2002), Tiryaki (2003)]. Nevertheless, the evidence here suggests

that there is a significant negative relationship between financial development and

investment volatility. This study also found that income level (gdp) has a relatively

robust negative relationship with consumption volatility.

The next interesting question would be whether capital market development

affects economic stability in some other ways. It may be the case that capital market

development affects the likelihood of a recession occurring, or its depth. Moreover,

little is known about the mechanism by which the deepening of capital markets affects

aggregate volatility. These are interesting topics for future research.

Page 21 of 36

References Acemoglu, Daron and Zilibotti, Fabrizio. "Was Prometheus Unbound by Chance?

Risk, Diversification, and Growth." Journal of political economy, 1997, 105(4), pp. 709-51.

Aghion, Philippe; Banerjee, Abhijit and Piketty, Thomas. "Dualism and

Macroeconomic Volatility." Quarterly Journal of Economics, 1999, 114(4), pp. 1359-97.

Barro, Robert J. and Lee, Jong-Wha. "International Data on Educational

Attainment: Updates and Implications." CID Working Paper No. 42, 2000. Baxter, Marianne and King, Robert G. "Measuring Business Cycles Approximate

Band-Pass Filters for Economic Time Series." NBER working paper 5022, 1995.

Beck, Thorsten and Levine, Ross. "Stock Markets, Banks, and Growth: Panel

Evidence." NBER working paper 9082, 2002. Beck, Thorsten; Levine, Ross and Loayza, Norman. "Finance and the Sources of

Growth." Journal of Financial Economics, 2000, 58(1-2), pp. 261-300. Beck, Thorsten; Lundberg, Mattias and Majnoni, Giovanni. "Financial

Intermediary Development and Growth Volatility: Do Intermediaries Dampen or Magnify Shocks?" World Bank Policy Research Working Paper Series No.2707, 2003.

Bernanke, Ben and Gertler, Mark. "Inside the Black Box: The Credit Channel of

Monetary Policy Transmission." Journal of Economic Perspectives, 1995, 9(4), pp. 27-48.

Bernanke, Ben; Gertler, Mark and Gilchrist, Simon. "The Financial Accelerator in

a Quantitative Business Cycle Framework." NBER working paper No. 6455, 1998.

Buch, Claudia M.; Doepke, Joerg and Pierdzioch, Christian. "Financial Openness

and Business Cycle Volatility." Journal of International Money and Finance, 2005, 24, pp. 744-65.

Christiano, Lawrence J. and Fitzgerald, Terry J. "The Band Pass Filter."

International Economic Review, 2003, 44(2), pp. 435-65. Demirguc-kunt, Asli and Levine, Ross. Financial Structure and Economic Growth:

A Cross-Country Comparison of Banks, Markets, and Development. Cambridge, Massachusetts, USA: The MIT Press, 2001.

Page 22 of 36

Denizer, Cevdet; Iyigun, Murat F. and Owen, Ann L. "Finance and Macroeconomic Volatility." Board of Governors of the Federal Reserve System. International Finance Discussion Papers No.670, 2000.

Easterly, William; Islam, Roumeen and Stiglitz, Joseph E. "Shaken and Stirred:

Explaining Growth Volatility," B. Pleskovic and J. E. Stiglitz, Annual Bank Conference on Development Economics. Washington D.C., 2000.

Fecht, Falko. "On the Stability of Different Financial Systems." Journal of European

Economic Association, 2004, 2(6), pp. 969-1014. Greenwald, Bruce C. and Stiglitz, Joseph E. "Financial Market Imperfections and

Business Cycles." Quarterly Journal of Economics, 1993, 108(1), pp. 77-114. Haan, Wouter J. den; Ramey, Garey and Watson, Joel. "Liquidity Flows and

Fragility of Business Enterprises." NBER working paper No. 7057, 1999. Haug, Alfred A. and Dewald, William G. "Longer-Term Effects of Monetary

Growth on Real and Nominal Variables, Major Industrial Countries, 1880-2001." European Central Bank working paper series No.382, 2004.

Hodrick, Robert J. and Prescott, Edward C. "Postwar U.S. Business Cycles: An

Empirical Investigation." Journal of Money, Credit, and Banking, 1997, 29, pp. 1-16.

Karras, Georgios and Song, Frank. "Sources of Business-Cycle Volatility: An

Exploratory Study on a Sample of Oecd Countries." Journal of Macroeconomics, 1996, 18(4), pp. 621-37.

La-Porta, Rafael; Lopez-de-Silane, Florencio; Shleifer, Andrei and Vishny,

Robert W. "Law and Finance." Journal of political economy, 1998, 106(6), pp. 1113-55.

Larrain, Borja. "Financial Development, Financial Constraints, and the Volatility of

Industrial Output." Public policy discussion paper, Federal Reserve Bank of Boston, 2004.

Levine, Ross. "Bank-Based or Market-Based Financial Systems: Which Is Better?"

NBER working paper No.9138, 2002. Levine, Ross and King, Robert G. "Finance and Growth: Schumpeter Might Be

Right." Quarterly Journal of Economics, 1993, 108, pp. 717-38. Levine, Ross; Loayza, Norman and Beck, Thorsten. "Financial Intermediation and

Growth: Causality and Causes." World Bank Policy Research Working Paper Series No.2059, 1999.

Lopez, Jose A. and Spiegel, Mark M. "Financial Structure and Macroeconomic

Performance over the Short and Long Run." Pacific Basin Working Paper series No.PB02-05, 2002.

Page 23 of 36

Merton, Robert C. and Bodie, Zvi. "The Design of Financial Systems: Towards a

Synthesis of Function and Structure." Harvard Business School Working Paper No. 02-074, 2004.

Phumiwasana, Triphon. "Financial Structure, Economic Growth and Stability,"

CLAREMONT GRADUATE UNIVERSITY, 2003, 131. Raddatz, Claudio. "Are External Shocks Responsible for the Instability of Output in

Low-Income Countries?" World Bank Policy Research Working Paper Series No. 3680, 2005.

____. "Liquidity Needs and Vulnerability to Financial Underdevelopment." World

Bank Policy Research Working Paper Series No. 3161, 2003. Rajan, Raghuram and Zingales, Luigi. "Financial Systems, Industrial Structure,

and Growth." Oxford Review of Economic Policy, 2001, 17(4), pp. 467-81. Silva, Gisele Ferreira da. "The Impact of Financial System Development on

Business Cycles Volatility: Cross-Country Evidence." Journal of Macroeconomics, 2002, 24(2), pp. 233-53.

Stock, James H. and Watson, Mark W. "Business Cycle Fluctuations in Us

Macroeconomic Time Series." NBER working paper No. 6528, 1998. Tiryaki, Gisele Ferreira. "Financial Development and Economic Fluctuations."

Middle East Technical University Studies in Development, 2003, 30(1), pp. 89-106.

Page 24 of 36

Table 1: Business Cycle Volatility (%) classified by Income Level. (Data cover six 5-year time span from 1975-2004 for 44 countries)

Standard deviation of growth rate (%) Standard deviation of filtered log GDP p.c. (%)COUNTRY Mean Median Max Min Mean Median Max Min High Income 1.8 1.6 6.4 0.3 1.2 1.0 3.9 0.2Australia 1.8 1.4 4.1 0.7 1.1 1.0 2.4 0.4Belgium 1.6 1.8 1.9 0.9 0.9 1.0 1.2 0.6Canada 1.8 1.4 3.3 0.7 1.3 1.2 2.0 0.6Denmark 1.8 2.0 2.4 0.3 1.0 1.1 1.4 0.5Finland 1.9 1.6 3.0 1.1 1.3 1.3 2.1 0.4France 1.0 1.1 1.5 0.5 0.7 0.7 0.8 0.5Germany 1.2 1.1 2.7 0.6 1.0 1.0 1.4 0.5Greece 1.8 1.8 2.9 0.6 1.1 1.1 2.3 0.2Iceland 2.6 3.0 3.2 1.3 1.3 1.6 1.9 0.3Ireland 3.0 3.0 3.7 2.0 1.8 1.9 2.3 1.4Israel 2.5 2.3 4.5 1.2 1.7 1.9 2.5 0.5Italy 1.1 1.1 1.8 0.4 0.7 0.7 1.1 0.4Japan 1.3 1.5 2.0 0.6 0.8 0.9 1.3 0.2Korea, South 2.7 1.8 6.0 1.2 1.9 1.7 3.9 0.8Netherlands 1.6 1.6 2.0 1.2 1.0 0.9 2.0 0.5New Zealand 2.1 1.9 3.3 0.8 1.1 1.0 2.1 0.6Norway 1.4 1.3 2.2 1.0 1.0 0.9 1.7 0.5Portugal 1.8 2.2 2.5 0.4 0.9 0.9 1.1 0.7Singapore 3.9 3.8 6.4 0.8 2.5 2.9 3.3 1.4Spain 1.4 1.3 2.2 0.8 0.6 0.6 1.1 0.2Sweden 1.3 1.3 2.1 0.3 0.9 0.9 1.3 0.5Switzerland 1.7 1.6 2.6 1.1 1.2 1.1 1.8 0.7United Kingdom 1.2 1.0 2.2 0.5 1.0 1.1 1.2 0.6United States 1.7 1.3 4.0 0.8 1.3 1.1 2.2 0.8Upper Middle Income 3.9 3.3 10.2 0.6 2.7 2.1 6.5 1.0Argentina 6.1 6.2 8.8 3.5 4.2 4.0 5.6 2.9Brazil 3.2 2.8 5.4 1.6 1.9 1.6 3.8 1.1Chile 4.3 3.7 7.4 2.7 2.7 1.9 5.7 1.6Malaysia 3.2 2.9 7.2 0.6 2.3 2.1 4.6 1.1Mexico 3.1 2.9 5.3 1.0 1.7 1.6 2.7 1.2South Africa 2.0 1.7 3.9 0.8 1.7 1.7 2.1 1.0Uruguay 4.7 3.6 9.1 1.9 3.3 2.9 5.4 2.2Venezuela 4.9 4.3 10.2 2.2 3.4 3.2 6.5 1.9Lower Middle Income 3.4 2.6 7.9 0.7 2.2 2.1 5.8 0.3Columbia 1.8 1.6 3.6 0.7 1.0 0.9 2.1 0.3Ecuador 2.6 2.6 3.6 1.6 1.6 1.6 2.0 0.9Indonesia 4.1 2.6 7.9 2.1 2.8 1.9 5.5 1.2Morocco 4.5 4.5 7.1 2.0 2.5 2.6 3.4 1.3Philippines 3.3 2.5 5.8 2.0 2.6 2.5 3.8 1.2Thailand 2.8 2.0 7.6 0.9 1.8 1.5 4.6 0.4Turkey 4.8 4.7 5.9 3.6 2.9 2.7 3.7 2.3

Page 25 of 36

Table 1 (continued) Standard deviation of growth rate (%) Standard deviation of filtered log GDP p.c. (%)

COUNTRY Mean Median Max Min Mean Median Max Min Low Income 3.2 2.6 9.0 0.4 2.2 1.8 5.7 0.4China 3.1 3.4 5.5 0.4 1.9 1.9 3.8 0.4Cote d'lvoire 2.9 2.6 6.5 0.7 2.4 2.5 4.3 0.5India 2.4 2.1 5.6 1.2 1.8 1.6 3.4 0.8Nigeria 5.3 4.5 9.0 3.2 3.4 3.3 5.7 1.4Pakistan 2.1 1.9 3.1 1.6 1.5 1.6 2.1 0.9All 2.6 2.1 10.2 0.3 1.7 1.4 6.5 0.2

Table 2: Capital Market Development among countries (Data cover last panel period of year 2000-2004 for 44 countries)

COUNTRY Private Credit

Ratio Turnover

Ratio

Financial Structure

Index High Income 113.6 91.9 1.3Australia 93.6 69.8 1.7Belgium 76.4 21.3 0.8Canada 81.0 65.1 1.8Denmark 147.8 69.7 0.6Finland 61.0 94.3 2.7France 87.6 81.5 1.5Germany 116.2 118.1 0.8Greece 66.7 42.0 1.2Iceland 125.0 57.6 1.0Ireland 117.2 40.1 0.7Israel 91.2 62.7 1.2Italy 82.7 105.8 1.1Japan 151.6 79.2 0.3Korea, South 97.6 289.4 1.5Netherlands 149.9 120.9 1.6New Zealand 115.4 41.3 0.2Norway 68.6 88.3 1.1Portugal 146.7 55.0 0.3Singapore 115.2 54.1 2.1Spain 108.7 179.0 1.5Sweden 91.7 112.0 1.9Switzerland 158.4 86.2 2.1United Kingdom 143.4 103.4 1.8United States 232.4 169.7 1.3Upper Middle Income 57.3 22.1 0.9Argentina 16.3 6.5 1.0Brazil 35.4 34.6 0.4Chile 64.0 8.7 1.1Malaysia 141.2 30.3 1.2Mexico 17.3 27.4 0.7South Africa 124.1 45.5 1.6Uruguay 49.4 n.a. n.a.Venezuela 11.0 1.7 0.0

Page 26 of 36

Table 2 (continued)

COUNTRY Private Credit

Ratio Turnover

Ratio

Financial Structure

Index Lower Middle Income 39.0 75.0 0.2Columbia 24.5 4.0 -0.6Ecuador 24.2 2.0 -1.3Indonesia 20.4 229.5 1.0Morocco 56.1 8.0 -0.3Philippines 37.1 11.5 0.6Thailand 101.4 96.8 0.8Turkey 19.2 173.2 1.4Low Income 44.4 125.8 0.6China 134.8 100.1 0.4Cote d'lvoire 14.7 1.7 -0.4India 31.9 145.1 1.4Nigeria 15.4 10.5 0.5Pakistan 25.3 371.7 1.2All 82.6 81.7 1.0

Table 3: Descriptive Statistics Mean Median Maximum Minimum Std. Dev. ObservationsG-VOL 0.7 0.8 2.3 -1.3 0.7 270B-VOL 0.3 0.3 1.9 -1.5 0.7 270C-VOL 1.1 1.0 8.2 -0.9 1.0 247I-VOL 2.3 2.2 5.6 -0.1 0.7 251TURNOVER 3.2 3.5 5.9 -1.0 1.3 230STRUC 0.0 0.2 2.7 -4.8 1.3 225CREDIT 3.9 4.0 5.4 -0.1 0.8 269GDP 9.1 9.4 10.5 6.5 1.0 270OPENNESS 4.0 4.0 5.8 2.3 0.6 270GCON 16.2 15.5 38.7 0.0 5.7 270SD-DREER 7.6 5.3 47.7 0.5 7.3 222SD-DTOT 7.0 4.6 44.6 0.6 6.9 242

Page 27 of 36

Table 4: Selected pairwise correlations

G-VOL B-VOL C-VOL I-VOL TURNOVER STRUC CREDIT GDP OPENNESS GCON SD-DREER SD-DTOT G-VOL 1.00 B-VOL 0.82 1.00 C-VOL 0.40 0.40 1.00 I-VOL 0.45 0.45 0.68 1.00 TURNOVER -0.25 -0.27 -0.23 -0.40 1.00 STRUC -0.17 -0.15 -0.31 -0.27 0.57 1.00 CREDIT -0.37 -0.35 -0.32 -0.40 0.45 0.40 1.00 GDP -0.38 -0.43 -0.38 -0.27 0.37 0.37 0.58 1.00 OPENNESS -0.03 -0.04 -0.07 0.01 -0.01 0.34 0.23 0.24 1.00 GCON -0.33 -0.34 -0.21 -0.17 0.07 0.16 0.31 0.52 0.28 1.00 SD-DREER 0.33 0.38 0.28 0.34 -0.30 -0.17 -0.33 -0.39 -0.17 -0.24 1.00 SD-DTOT 0.20 0.23 0.21 0.23 -0.34 -0.38 -0.45 -0.50 -0.22 -0.36 0.46 1.00

Page 28 of 36

Table 5: Growth Volatility - using absolute measure of capital market development

G-VOL Pool RE FE FEI IV IVFE TURNOVER -0.10 *** -0.11*** -0.17** -0.16** -0.29 ** -0.03 (0.04) (0.04) (0.08) (0.07) (0.13) (0.22) CREDIT -0.07 -0.04 0.12 0.08 0.26 -1.02 (0.12) (0.13) (0.19) (0.18) (0.33) (0.70) GDP -0.11 -0.13 0.39 0.61 -0.14 0.87 (0.09) (0.10) (0.43) (0.38) (0.14) (0.86) OPENNESS 0.31 *** 0.28*** -0.63** -0.70*** 0.24 *** -0.54 (0.07) (0.08) (0.29) (0.27) (0.09) (0.44) GCON -0.03 *** -0.03*** 0.03 0.03 -0.03 *** 0.03 (0.01) (0.01) (0.02) (0.02) (0.01) (0.03) SD-DREER 0.02 *** 0.02*** 0.01 0.01 0.03 *** 0.05*** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT -0.02 * -0.02* -0.02 -0.01 -0.03 ** -0.02 (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) Observations 177.00 177.00 177.00 177.00 163.00 149.00 No. of countries - 44.00 44.00 44.00 - 40.00 R2 0.30 0.03 0.12 0.14 0.23 0.00 F / Chi2 10.48 *** 53.43*** 3.33*** 3.78*** 11.56 *** 177.64***Fu / Chi2u - 0.35 1.74*** 1.86*** - 1.04 Correlation(Xb, ui) - - -0.86 -0.91 - -0.80 Hausman - 31.35*** - - - - J stat - - - 0.45 0.05

Note: robust standard error in parenthesis. * sig. at 10%, ** sig. at 5%, *** sig. at 1% Pool= pooled estimation, RE = random effects, FE= fixed effects, FEI= fixed effects using initial value data of lturnover, lcredit and lgdp, IV= instrumental variable estimation (instruments: time trend, legal origin, creditor's protection index), IVFE= fixed effects instrumental estimation (instruments: time trend, creditor's protection index, human capital index) R2 = R2 or Within-R2 [squared correlation between ( )it iy y− and ˆ( ).it ix x β− ] F / Chi2 = F or Chi2 statistics for testing sig. of all Xs except constant Fu / Chi2u = F or Chi2 statistics for testing sig. of cross-sectional individual effects, Corr(Xb,ui)= correlation of predicted valued (Xb) and individual fixed effects (ui) Hausman = Hausman Chi2 statistics for testing of no-correlation between ui and Xs J stat = J statistics for GMM overeidentifying test

Page 29 of 36

Table 6: Growth Volatility - using relative measure of capital market development

G-VOL Pool RE FE FEI IV IVFE STRUC -0.07 ** -0.08** -0.16** -0.19*** -0.13 * -0.04 (0.04) (0.04) (0.07) (0.07) (0.08) (0.19) CREDIT -0.12 -0.09 0.01 -0.05 0.07 -1.05 (0.12) (0.13) (0.19) (0.19) (0.30) (0.72) GDP -0.11 -0.13 0.57 0.87** -0.18 0.94 (0.09) (0.10) (0.41) (0.40) (0.14) (0.99) OPENNESS 0.36 *** 0.34*** -0.53* -0.52 0.39 *** -0.50 (0.08) (0.09) (0.30) (0.33) (0.07) (0.49) GCON -0.03 *** -0.02** 0.03 0.03 -0.03 *** 0.03 (0.01) (0.01) (0.02) (0.02) (0.01) (0.03) SD-DREER 0.03 *** 0.03*** 0.02* 0.02* 0.03 *** 0.05*** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT -0.02 -0.02* -0.02 -0.02 -0.02 ** -0.02 (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) Observations 177.00 177.00 177.00 173.00 164.00 150.00 No. of countries - 44.00 44.00 45.00 - 41.00 R2 0.29 0.03 0.12 0.13 0.29 0.00 F / Chi2 11.11 *** 56.41*** 3.04*** 2.87*** 13.21 *** 182.97***Fu / Chi2u - 0.45 1.80*** 1.79*** - 1.09 Correlation(Xb, ui) - - -0.90 -0.93 - -0.81 Hausman - 32.99*** - - - - J stat - - - - 3.22 0.02


Page 30 of 36

Table 7: Business Cycle Volatility - using absolute measure of capital market development

B-VOL Pool RE FE FEI IV IVFE TURNOVER -0.10 *** -0.12*** -0.15*** -0.14*** -0.39 *** -0.01 (0.04) (0.04) (0.06) (0.05) (0.12) (0.16) CREDIT 0.02 0.10 0.15 0.10 0.73 ** -0.07 (0.11) (0.12) (0.15) (0.13) (0.37) (0.51) GDP -0.15 * -0.22** 0.32 0.50* -0.29 ** 0.00 (0.08) (0.11) (0.34) (0.29) (0.15) (0.62) OPENNESS 0.26 *** 0.03 -1.02*** -1.07*** 0.18 * -1.16*** (0.08) (0.13) (0.28) (0.27) (0.10) (0.32) GCON -0.02 0.00 0.05** 0.05** -0.02 0.03 (0.01) (0.01) (0.02) (0.02) (0.01) (0.03) SD-DREER 0.03 *** 0.02*** 0.02** 0.02** 0.03 *** 0.04*** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT -0.01 -0.01 0.00 0.00 -0.02 -0.02 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Observations 177.00 177.00 177.00 177.00 163.00 149.00 No. of countries - 44.00 44.00 44.00 - 40.00 R2 0.31 0.10 0.24 0.25 - 0.28 F / Chi2 8.17 *** 32.03*** 5.56*** 5.77*** 8.83 *** 78.75***Fu / Chi2u - 7.98*** 3.62*** 3.81*** - 3.13***Correlation(Xb, ui) - - -0.86 -0.89 - -0.81 Hausman - 22.00*** - - - - J stat - - - - 0.35 0.04


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Table 8: Business Cycle Volatility - using relative measure of capital market development

B-VOL Pool RE FE FEI IV IVFE STRUC -0.03 -0.06 -0.09 -0.12 ** -0.20 ** -0.02 (0.04) (0.05) (0.06) (0.05) (0.09) (0.14) CREDIT -0.04 0.03 0.08 0.00 0.54 -0.08 (0.12) (0.13) (0.15) (0.13) (0.36) (0.52) GDP -0.17 ** -0.24 ** 0.33 0.59 * -0.36 ** 0.04 (0.09) (0.12) (0.33) (0.31) (0.16) (0.71) OPENNESS 0.29 *** 0.08 -1.01 *** -0.97 *** 0.37 *** -1.14 *** (0.09) (0.13) (0.30) (0.33) (0.09) (0.36) GCON -0.01 0.00 0.05 ** 0.05 ** -0.02 0.04 (0.01) (0.01) (0.02) (0.02) (0.01) (0.03) SD-DREER 0.03 *** 0.03 *** 0.02 ** 0.02 ** 0.04 *** 0.04 *** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT -0.01 -0.01 -0.01 -0.01 -0.01 -0.02 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Observations 177.00 177.00 177.00 173.00 164.00 150.00 No. of countries - 44.00 44.00 45.00 - 41.00 R2 0.29 0.08 0.22 0.22 0.08 0.28 F / Chi2 8.20 *** 31.42 *** 4.25 *** 4.18 *** 9.49 *** 82.41 ***Fu / Chi2u - 8.75 *** 3.64 *** 3.56 *** - 3.42 ***Correlation(Xb, ui) - - -0.88 -0.91 - -0.81 Hausman - 113.44 *** - - - - J stat - - - - 4.07 0.03


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Table 9: Investment Volatility - using absolute measure of capital market development

I-VOL Pool RE FE FEI IV IVFE TURNOVER -0.19 *** -0.18 *** -0.17 *** -0.15 *** -0.32 *** -0.31 * (0.05) (0.05) (0.06) (0.06) (0.11) (0.17) CREDIT -0.28 *** -0.20 * -0.13 -0.12 -0.03 -0.40 (0.11) (0.11) (0.13) (0.14) (0.33) (0.57) GDP 0.11 -0.07 -0.36 -0.21 0.06 0.58 (0.09) (0.12) (0.43) (0.38) (0.17) (0.84) OPENNESS 0.14 0.00 -0.09 -0.20 0.11 -0.45 (0.12) (0.16) (0.31) (0.31) (0.11) (0.38) GCON -0.02 * 0.00 0.04 0.04 -0.03 ** 0.00 (0.01) (0.02) (0.03) (0.03) (0.01) (0.03) SD-DREER 0.02 *** 0.02 *** 0.02 ** 0.02 *** 0.02 ** 0.03 *** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT 0.00 0.00 0.00 0.00 0.00 -0.01 (0.01) -(0.01) (0.01) (0.01) (0.01) (0.01) Observations 166.00 166.00 166.00 166.00 152.00 138.00 No. of countries - 42.00 42.00 42.00 - 38.00 R2 0.30 0.18 0.21 0.19 0.32 0.22 F / Chi2 11.75 *** 51.67 *** 4.53 *** 3.84 *** 10.37 *** 2576.02 ***Fu / Chi2u - 93.13 *** 4.78 *** 4.77 *** - 3.37 ***Correlation(Xb, ui) - - -0.27 -0.14 - -0.43 Hausman - -1.32 - - - - J stat - - - - 3.35 0.56


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Table 10: Investment Volatility - using relative measure of capital market development

I-VOL Pool RE FE FEI IV IVFE STRUC -0.13 * -0.12 * -0.11 * -0.15 *** -0.26 *** -0.31 * (0.07) (0.06) (0.06) (0.06) (0.08) (0.17) CREDIT -0.37 *** -0.29 *** -0.23 * -0.23 -0.04 -0.55 (0.10) (0.11) (0.14) (0.15) (0.32) (0.62) GDP 0.09 -0.09 -0.31 0.04 -0.01 1.00 (0.10) (0.13) (0.43) (0.42) (0.17) (1.03) OPENNESS 0.22 * 0.06 -0.06 -0.16 0.27 ** -0.14 (0.12) (0.17) (0.33) (0.36) (0.12) (0.44) GCON -0.01 0.01 0.04 0.05 -0.02 0.03 (0.01) (0.02) (0.03) (0.03) (0.01) (0.03) SD-DREER 0.03 *** 0.02 *** 0.02 *** 0.03 *** 0.03 *** 0.04 *** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT 0.00 0.00 -0.01 -0.01 0.00 -0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Observations 166.00 166.00 166.00 161.00 153.00 139.00 No. of countries - 42.00 42.00 42.00 - 39.00 R2 0.27 0.17 0.19 0.19 0.21 0.12 F / Chi2 8.75 *** 41.89 *** 4.10 *** 4.64 *** 9.40 *** 2328.14 ***Fu / Chi2u - 88.15 *** 4.84 *** 4.32 *** - 3.22 ***Correlation(Xb, ui) - - -0.19 -0.14 - -0.72 Hausman - 8.21 - - - - J stat - - - - 2.05 0.32


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Table 11: Consumption Volatility - using absolute measure of capital market development

C-VOL Pool RE FE FEI IV IVFE TURNOVER -0.09 -0.13 ** -0.11 -0.04 -0.35 ** -0.48 *** (0.06) (0.06) (0.08) (0.07) (0.18) (0.18) CREDIT -0.17 0.07 0.33 0.24 0.10 0.39 (0.12) (0.12) (0.21) (0.22) (0.45) (0.62) GDP -0.25 ** -0.48 *** -1.74 *** -1.44 ** -0.27 -0.54 (0.11) (0.17) (0.71) (0.66) (0.24) (0.92) OPENNESS 0.19 0.05 0.51 0.21 0.17 0.16 (0.11) (0.17) (0.42) (0.41) (0.13) (0.41) GCON -0.01 0.01 0.01 0.02 -0.02 -0.03 (0.02) (0.02) (0.04) (0.03) (0.02) (0.03) SD-DREER 0.02 *** 0.02 * 0.01 0.01 0.01 0.02 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT -0.01 -0.01 -0.01 -0.01 -0.02 -0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Observations 166.00 166.00 166.00 166.00 152.00 138.00 No. of countries - 42.00 42.00 42.00 - 38.00 R2 0.19 0.15 0.19 0.15 0.16 0.12 F / Chi2 10.46 *** 33.72 *** 3.68 *** 2.57 ** 12.43 *** 439.50 ***Fu / Chi2u - 169.30 *** 7.56 *** 7.14 *** - 6.58 ***Correlation(Xb, ui) - - -0.81 -0.70 - -0.45 Hausman - 4.53 - - - - J stat - - - - 3.64 0.23


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Table 12: Consumption Volatility - using relative measure of capital market development

C-VOL Pool RE FE FEI IV IVFE STRUC -0.20 * -0.16 -0.11 -0.08 -0.21 ** -0.46 ** (0.11) (0.10) (0.08) (0.07) (0.11) (0.19) CREDIT -0.18 0.00 0.25 0.19 0.21 0.17 (0.11) (0.12) (0.20) (0.23) (0.42) (0.68) GDP -0.20 -0.43 ** -1.61 *** -1.32 ** -0.41 * 0.04 (0.14) (0.19) (0.61) (0.68) -(0.22) (1.13) OPENNESS 0.30 *** 0.18 0.57 0.39 0.29 ** 0.62 (0.12) (0.19) (0.46) (0.44) (0.12) (0.48) GCON -0.01 0.01 0.01 0.02 -0.01 0.02 (0.02) (0.02) (0.04) (0.03) (0.02) (0.04) SD-DREER 0.03 *** 0.02 ** 0.01 0.02 *** 0.03 *** 0.03 ** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) SD-DTOT -0.02 -0.01 -0.01 -0.01 -0.02 -0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) Observations 166.00 166.00 166.00 161.00 153.00 139.00 No. of countries - 42.00 42.00 42.00 - 39.00 R2 0.22 0.15 0.19 0.19 0.21 0.00 F / Chi2 11.04 *** 33.75 *** 3.25 *** 3.77 *** 13.93 *** 385.87 ***Fu / Chi2u - 157.02 *** 7.09 *** 6.77 *** - 5.49 ***Correlation(Xb, ui) - - -0.77 -0.65 - -0.32 Hausman - 6.01 - - - - J stat - - - - 5.00 * 0.42


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Appendix A: Variables Variables Description

g-vol log (sd. of growth rate of gdp per capita)

b-vol log (sd. of business cycle component of gdp per capita)

c-vol log (sd. of household consumption growth rate)

i-vol log (sd. of gross capital formation growth rate)

turnover log (turnover ratio) = log (value of shares traded / GDP)

struc financial structure- aggregate index

credit log (private credit ratio) = log (private credit / GDP)

gdp log (gdp per capita)

openness log (openness ratio) = log ([export + import] / GDP)

gcon government consumption over gdp ratio

sd-dreer sd. of changes in real effective exchange rate

sd-dtot sd. of changes in terms of trade

Appendix B: Countries covered (44) classified by Income Level High Income (24): Australia Belgium Canada Denmark Finland France Germany Greece Iceland Ireland Israel Italy Japan Korea Netherlands New_Zealand Norway Portugal Singapore Spain Sweden Switzerland United_Kingdom United_States Upper Middle Income (8): Argentina Brazil Chile Malaysia Mexico South_Africa Uruguay Venezuela Lower Middle Income (7): Columbia Ecuador Indonesia Morocco Philippines Thailand Turkey Low Income (5): Bangladesh Cote_d'lvoire India Nigeria Pakistan China

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Capital Market and Business Cycle Volatility · Capital Market and Business Cycle Volatility Piyapas Tharavanij1 ... reducing inequality of access and financial imperfection are necessary